{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "issue_url='https://github.com/matplotlib/mplfinance/issues/26'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.12.1a0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'0.25.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mplfinance as mpf\n",
    "mpf.__version__\n",
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('issue26_data.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-02-05 00:00:00</td>\n",
       "      <td>8314.5</td>\n",
       "      <td>8324.1</td>\n",
       "      <td>8292.4</td>\n",
       "      <td>8293.1</td>\n",
       "      <td>9.564241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05 00:30:00</td>\n",
       "      <td>8293.1</td>\n",
       "      <td>8313.6</td>\n",
       "      <td>8293.1</td>\n",
       "      <td>8307.9</td>\n",
       "      <td>2.535547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05 01:00:00</td>\n",
       "      <td>8307.7</td>\n",
       "      <td>8321.9</td>\n",
       "      <td>8295.7</td>\n",
       "      <td>8313.8</td>\n",
       "      <td>17.049031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close     Volume\n",
       "2020-02-05 00:00:00  8314.5  8324.1  8292.4  8293.1   9.564241\n",
       "2020-02-05 00:30:00  8293.1  8313.6  8293.1  8307.9   2.535547\n",
       "2020-02-05 01:00:00  8307.7  8321.9  8295.7  8313.8  17.049031"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-02-05 22:30:00</td>\n",
       "      <td>8809.6</td>\n",
       "      <td>8823.7</td>\n",
       "      <td>8801.8</td>\n",
       "      <td>8803.7</td>\n",
       "      <td>74.728087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05 23:00:00</td>\n",
       "      <td>8803.1</td>\n",
       "      <td>8805.1</td>\n",
       "      <td>8744.2</td>\n",
       "      <td>8754.4</td>\n",
       "      <td>83.626079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05 23:30:00</td>\n",
       "      <td>8753.1</td>\n",
       "      <td>8764.4</td>\n",
       "      <td>8730.9</td>\n",
       "      <td>8746.0</td>\n",
       "      <td>38.084380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close     Volume\n",
       "2020-02-05 22:30:00  8809.6  8823.7  8801.8  8803.7  74.728087\n",
       "2020-02-05 23:00:00  8803.1  8805.1  8744.2  8754.4  83.626079\n",
       "2020-02-05 23:30:00  8753.1  8764.4  8730.9  8746.0  38.084380"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f3Hf/vXv3Ehoaiq+vLwAxMTGkpqbSoEED0+w2ADs7O+zt7XF3d6dx48amGXEAqampBAcH07JlSzIyMjAajQAUFxeTkZFx1yU3IYQQtVOVk8+5c+cYPHgwDz/8sGlb586dGTRoEOfOnbvv/kFBQaSkpJCbe3umys6dO/Hy8iIuLo6EhAQuXbpESUkJa9eu5bHHHgMgOjqa5cuXA7cTT0pKCr169SI4OBgvLy82b94MwPr16/H396/y+JMQona48+ZRuZG0/qjymI+7uzu7du2if//+uLi4AJCfn09SUhI63f0HpHr27Mnx48cZMGAAGo0GnU7HnDlzaNu2LWPGjGHQoEE4ODjQqVMnXnnlFQBGjRrF+PHjeeKJJ9BqtcyYMYMmTZoAt6dhT5o0ifj4eDw9PeUpC0JYQHWfq1ad/STR1G9VTj6xsbHMmTOHLl264Ovri9Fo5OLFixQXF/Pqq69WqY6RI0cycuTIu7b37dvXNImhLJ1OR3x8fIV1hYSEsGbNmqo2X4h6514JoTY9vLNsG2pDe4Q6qpx8XnnlFfLz81m+fLlpHMbBwYFBgwZVmFCEEEKIylQ5+djZ2TFmzBhef/11zp8/T0lJCQEBATLDTIg6LDjAw9TbqInLYHfWmZqVK5fbBHCf5PPTTz8RFBREkyZN+Omnn+4qP3HihOnnRx991PKtE0JYRdnLchUlECHMdc/kExcXxzvvvEO/fv0YPHiw6WGid9JoNOUSkRCibrhzvKUqvZJ7Jaaqfo4Q90w+jzzyiGl2mfRshKg/KkoWcrlMWNI9k8+yZcsq/FkIUX9JYhKWUOWbTENDQ1mxYkVNtkUIUQMeZEVQWbJAqKXKyedPf/oTycnJpiW0hRDiTvK0AlFVVZ5q7enpydatW+nWrRtt2rQxPeUAbk84mDt3bo00UAhhHmtfJpOelKhIlZPPqlWrTD9fuXKlXFlls+CEELVTdRJCYdGt+z5eX2a1iaqqcvL597//LUlGCCGERVQ5+fTp04fi4mJcXV1rsj1CiDpIejniQd13wsHNmzd54403ePjhh3nkkUd44YUXuHjxohptE0IIYaPum3zmzZtHQkICJSUl2NnZsW/fPt5880012iaEqCJZF0fUNfdNPps2baJjx47s27ePQ4cOERcXR0pKCmlpaWq0TwjBve/Pud99O0LURvdNPlevXuWpp56iUaNGODg4MHjwYBRF4fLly2q0TwhRRXfONJPEI2qz+yYfo9GIs7Oz6XXp/T0lJSU11yohhBA2rUqz3YqKirhx4/b8/tL/6/V6089AlZbSFkIIIaCKyeedd97hnXfeMb3WaDTlVi+VJRWEEEI8iColn/s9z62qz3tbu3YtCxcuRFEUfHx8mDx5MkeOHGH69Ol4eXmZ3jd69GieeOIJ8vLymDBhAmfPnsXR0ZHhw4cTExMDwKlTp5g6dSq5ubl4eHgwdepU2rZtW6V2CFFblV0rRwhbdt/kc+rUKYt8UFpaGjNnzmTjxo14e3uzatUqJkyYwJNPPsmTTz7J9OnT79pn9uzZNGvWjPj4eLKysujfvz+dOnXC29ubN954g9GjRxMREcHWrVsZM2YMmzZtskhbhRBC1KwqP9XaXGlpabRo0QJvb28AunTpwtmzZ8nPz6dBgwYV7pOYmEhsbCwAAQEBhIWFkZSUxOnTp8nPzyciIgKAqKgosrOzZfq3sBlyz46wdVV+vI65OnbsSGZmJmfOnKF169Zs27aNrl27kpeXx6FDh+jfvz95eXn06NGDUaNGcfPmTa5du0ZgYKCpjsDAQM6dO4enpyf+/v7l6g8ICODcuXO0atWq3HYnBzv8mlp+MoSTg32N1FsXSOzqxF7R55QmosKiW7hoHe/7fkuS417/Yjcnbq2T/T3LVUs+3t7ejBo1ir59+6LT6XB2dmb58uWkpKTg4eFBXFwcRUVFDBs2jP/+97/069cPOzs7HB3/9w9Mq9WSk5NDYWEhWq22XP1arZaCgoK7PtdQbLzvk3irw6+prkbqrQsk9pqPPTUr1/Q5qVm5d/V+KmpDTbdLjnv9i92cuIsMJbhoK08xqiWfEydOMH/+fLZv346vry8bNmxg2LBhbN682fS0bGdnZ1544QX++9//8vzzz2M0GjEYDDg5OQG3p3e7urri6upKUVFRufr1ej1ubm5qhSOEqiqagCBLWIu6TLUxn7179xIaGoqvry8AMTExpKamcuTIEfLz803vUxQFBwcH3N3dady4Menp6aay1NRUgoODadmyJRkZGRiNRgCKi4vJyMi465KbEEKI2km15BMUFERKSgq5ubf/Wtu5cydeXl58+eWXzJo1C0VRKCoqYtWqVfTo0QOA6Oholi9fDtxOPCkpKfTq1Yvg4GC8vLzYvHkzAOvXr8ff35+goCC1whFCCGEG1S679ezZk+PHjzNgwAA0Gg06nY45c+YQHBzMpEmT6N27NxqNhu7du/Piiy8CMGrUKMaPH88TTzyBVqtlxowZNGnSBLg9DXvSpEnEx8fj6enJrFmz1ApFiFpF7gkSdZFqyQdg5MiR5Z6MUGru3LkVvl+n0xEfH19hWUhICGvWrLFo+4QQQqhDtctuQojb7rU8ghD1hSQfIYQQqpPkI4QQQnWSfIQQQqhOko8QQgjVSfIRQgihOlWnWgsh/qfs2j0y+03UN9LzEUIIoTrp+QhhJWWfTCAPCRX1jfR8hBBCqE6SjxBCCNVJ8hFCCKE6ST5CCCFUJxMOhKhFZHkEUV9Iz0cIIYTqJPkIIYRQnSQfIYQQqpPkI4QQQnWqJp+1a9cSExNDdHQ0Q4YMIT09vVz5P/7xDwYPHmx6ffHiRYYMGULv3r155pln2Ldvn6ls7969PPPMM/Tu3ZshQ4Zw6dIl1eIQQghhHtWST1paGjNnzmTRokUkJCQQGRnJhAkTTOU7d+7k2LFj5faZNGkS3bt3JzExkWnTpjF69Gj0ej0FBQWMGjWKd955h8TERP74xz8ydepUtUIRQghhJlWTT4sWLfD29gagS5cunD17FoDCwkJmzpzJiBEjTO/Pz88nOTmZ/v37A9ChQweaNWtGcnIy+/btIyAggHbt2gEQGxvL7t27uXHjhlrhCCGEMINqyadjx45kZmZy5swZFEVh27ZtdO3aFYD4+Hj+/Oc/4+fnZ3r/+fPn8fDwwNXV1bQtMDCQ9PR0MjIyCAgIMG13c3PD3d2dzMxMtcIRKgsO8Cj30M07X1tqHyGEOlS7ydTb25tRo0bRt29fdDodzs7OLF++nDNnzrB7926+/vprfv75Z9P79Xo9Wq22XB1arZaCggIURam07E5ODnb4NdVZPB4nB/saqbcuUDt2F62j6ec7Pzc4wIPColsW2acqHiT20jZU9ll17fdHfufrX+zmxK11sr9nuWrJ58SJE8yfP5/t27fj6+vLhg0bGDZsGI0aNWLSpEk4OjqWe7+LiwtFRUXltun1elxdXVEUpcIyNze3uz7XUGzkwmXLX47za6qrkXrrAmvFnpqVa/rcsguw3ast1dnnXqoTu4vWscIlE+ra74/8zte/2M2Ju8hQgou28hSjWvLZu3cvoaGh+Pr6AhATE8PYsWPRarW8/vrrANy6dYuCggKeeuopVq1aRW5uLnl5eTRs2BCA1NRU+vXrR0lJCZs2bTLVnZOTw/Xr12nevLla4QgLu/NyWF1a3bNsO+tSu4WwJtXGfIKCgkhJSSE39/ZfgDt37sTLy4vDhw+zZ88e9uzZw9y5cwkNDWXTpk3odDrCw8NZsWIFcDt55ebmEhYWRufOnbl06RIHDhwAYNmyZTz++OPlxoeEbbH0M8/Kjv+U/lydpHG/fVKzcitcNE6I+k61nk/Pnj05fvw4AwYMQKPRoNPpmDNnDhqNptJ9pk2bxrhx41i3bh06nY6PP/4YJycnAD788EOmT59OYWEhgYGBvPfee2qFImpQdU7UpQnAEr2O4ACPaiWIB2m3JCAhVH6q9ciRIxk5cmSl5Z07d6Zz586m1z4+PixZsqTS927cuNHibRR1hznJRpawFsK6ZEkFUafd2YuoiURSl8ejhKit5NluQlVyr40QAqTnI+oZc8ZbUrNyTVNPZdxGCPNIz0cIIYTqpOcjbJI5PRNLzp4TQlRMej7CKu585lptIJfShFCP9HzEfd15B3/Z56ZVdMKuy7PDKps9J4SwLEk+tVjZyz/WbsO9ymv6pkwhhO2R5COqpGyCKCy6xYXLN6r0aJmKfhZCCBnzqQUqerZYXblMBbVz/EYIUbtJ8hFy46cQQnWSfGoRNS5T1UQPSy6vCSEelIz51COW7t1UlmgkAQkh7kd6PvVQZT2Vita4EUKImiA9nzrgXnfcSy9DCFEXSfIRJjJ2I4RQiySfalDr5s+K6q+J9Wok0Qgh1CZjPiqRMRQhhPgf6fk8AEskD0s/MfnOx9vU5eeqCSHqD1V7PmvXriUmJobo6GiGDBlCeno6t27dYurUqURFRdG7d28mT57MrVu3AMjLy2PEiBH07t2bPn36sGXLFlNdp06dIjY2lt69exMbG8upU6fUDKVSavVwqvJATyGEqK1U6/mkpaUxc+ZMNm7ciLe3N6tWrWLChAn06NGDnJwcvvnmG4qLi4mLi2PNmjU899xzzJ49m2bNmhEfH09WVhb9+/enU6dOeHt788YbbzB69GgiIiLYunUrY8aMYdOmTTUaw4OMt5Ttkdz5VOjq1Hev9tyvrCbGiYQQwhyq9XzS0tJo0aIF3t7eAHTp0oWzZ8/y6KOPMnr0aOzt7dFqtTz88MOkp6cDkJiYSGxsLAABAQGEhYWRlJTE6dOnyc/PJyIiAoCoqCiys7NJS0tTKxyLSs3Ktcig//3qsNTnCCGEuVRLPh07diQzM5MzZ86gKArbtm2ja9euPPzwwzRv3hyAy5cvs2vXLh5//HFyc3O5du0agYGBpjoCAwM5d+4cGRkZ+Pv7l6s/ICCAc+fOqRXOfVlr2rIkGCFEXaDaZTdvb29GjRpF37590el0ODs7s3z5clP5c889x9GjRxkyZAhdu3bl0qVL2NnZ4ej4v4XLtFotOTk5FBYWotVqy9Wv1WopKCi463OdHOzwa6qrkZjuVW9lZTXVFjU5OdjbRBzVIbFL7PWJOXFrnezvWa5a8jlx4gTz589n+/bt+Pr6smHDBoYNG8bmzZvRaDSsWLGCGzdu8NZbbzF79mxefvlljEYjBoMBJycnAPR6Pa6urri6ulJUVFSufr1ej5ub212fayg2cuHyjRqJqXRFz4pmlN35maW9kZpqi5r8mupsIo7qkNgl9vrEnLiLDCW4aCtPMapddtu7dy+hoaH4+voCEBMTQ2pqKuvXr+fixYsA6HQ6nnnmGXbv3o27uzuNGzc2jf8ApKamEhwcTMuWLcnIyMBoNAJQXFxMRkYGrVq1UiWWqoytCCGEqJxqyScoKIiUlBRyc2+fmHfu3ImXlxf79+9n7ty5GI1GFEXh+++/JyQkBIDo6GjTpbnU1FRSUlLo1asXwcHBeHl5sXnzZgDWr1+Pv78/QUFBaoVDYdGtcuMrpT9L4hFCiPtT7bJbz549OX78OAMGDECj0aDT6ZgzZw7BwcFMnz6d6OhoFEUxvQYYNWoU48eP54knnkCr1TJjxgyaNGkCwOzZs5k0aRLx8fF4enoya9YstUIRQghhJo2iKIq1G1FTevToQWFRMSu/2mzxuuvrNWCQ2CX2+qe+xm5O3IOe7YOL1oHvv/++wnJ5tpsQQgjVSfIRQgihOkk+QgghVGfTT7U+e/YshYVF/LR/r8XrznB34cq1QovXWxdI7BJ7fVNfYzcn7rOnT+Hioq203KaTD0Bg8xY0a+Zn8XobN9Li4FJ0/zfaIIldYq9v6mvs5sQd2LwFVy7/Wmm5TSef1q1bU1hUjH9A4P3f/IDq6+wXkNgl9vqnvsZuTtxaZ2dat25dabmM+QghhFBdrUg+iYmJPP3000RFRTFw4EDOnDkDwOLFi4mOjqZ3795MnDgRg8EAgMFgYOLEifTu3ZuYmBiWLl1qzeYLIYR4QFZPPhcvXmTKlCnMmzePrVu3EhUVxYQJEzh06BBLly5l9erVJCQkkJ2dbXrUzuLFi7l+/ToJCQmsWLGCxYsXc7B1zUYAAB8TSURBVPToUStHIoQQoqqsnnwcHBx4//338fO7PSngscceIz09na1btxITE0PDhg2xs7Nj4MCBJCQkALB161b69++PnZ0dHh4eREVFsXXrVmuGIYQQ4gFYPfk0bdqU8PBw4PbTqf/v//6PXr16kZGRUW4hubKLxaWnp1e4yJwQQoi6odbMdluyZAnz5s0jMDCQTz75hDFjxpjW8QFwdnamsPD2fHO9Xl9uMbmyZXeqqcXk6uviUiCxS+y2re3kxHKvT03vXW9iv5NNLCZ3P3/729+Ii4vjm2++ITY2lubNm5smGAAUFhbi6uoKgIuLS7nF5MqW3ammFpOrr1MvQWKX2OuXC5dv1NvYbWIxucqkpaXx448/AqDRaOjTpw83b95Eo9GUu5RWupAcQMuWLSstE0IIUftZPfnk5OQwduxYfvvtNwAOHjzIrVu3GDZsmGmWW3FxMStXruTJJ58Ebi8yt3LlSkpKSrh8+TKJiYnExMRYMwwhhBAPwOqX3R599FH+/ve/M2TIEIxGI05OTnz44Yc8+uijDB06lEGDBqEoCl27dmXgwIEAxMXFce7cOaKiorC3t2fEiBG0bdvWypEIIYSoKqsnH4Dnn3+e559//q7tcXFxxMXF3bXd0dGRGTNmqNE0IYQQNcBil93y8vI4evQo165do7i4GBteIFUIIYSZzE4+BoOBt956i/DwcPr378+xY8c4fvw4/fr148qVK5ZooxBCCBtjdvL597//zYYNGwgNDTVtKy4u5tSpU7z33nvmVi+EEMIGmZ18tmzZwtChQ/nggw9Ml9o6derE0KFD2bdvn9kNFEIIYXvMTj4ajYYGDRpUWFZcXGxu9UIIIWyQ2bPdHnroIT777DPTfTrbtm0jISGBDRs20LlzZ7MbKIQQwvaY3fMZM2YMdnZ2rFixAoCvvvqKtWvX4urqyoQJE8xuoBBCCNtjds+nbdu2bNmyhfXr15ORkYFWqyUoKIioqCg8PT0t0UYhhBA2xiI3mXp6ejJ06FBLVCWEEKIeMDv5pKWl8e6773Ls2DFu3Lj76afHjh0z9yOEEELYGLOTz5tvvsnJkyct0RYhhBD1hNnJJz09nW7dujF58mS8vb2xs7P6g7KFEELUcmYnn4iICDw8PPD39692HUlJSXz88ccYDAbc3d2ZNm0abdq0YfHixaxevRqj0cgjjzzClClTcHJywmAwMG3aNA4cOIC9vT2xsbEVPoBUCCFE7WR28pkyZQqDBw+mf//+tGrVCgeH/1Wp0WiYPn36Pff/7bffGD9+PKtWrSI4OJgVK1YwefJkxo8fz9KlS1m/fj06nY7hw4ezfPlyXnzxRRYvXsz169dJSEjg+vXr9OvXj9DQUNq3b29uOEIIIVRgdvKZMGECp06dAuDIkSPlyqqSfBwcHHj//fdNK5F26tSJDz/8kK1btxITE0PDhg0BGDhwIPHx8bz44ots3bqVf/7zn9jZ2eHh4UFUVBRbt26V5COEDesVv8f0c9KIcCu2RFiC2cnnhx9+oEmTJgwYMABvb280Gs0D7e/p6Um3bt1Mr3ft2kXHjh3JyMigZ8+epu0BAQGmpbPT09MJDAw0lQUGBrJz504zIxFCCKEWs5NPixYt6NOnDy+99JLZjdm7dy9LlixhyZIlvP322zg5OZnKnJ2dKSwsBECv16PVaissu5OTgx1+TXVmt+3ueu1rpN66QGKX2K1NzXb4NdXVqtjVZE7cWif7e5ZbZMznP//5D15eXoSEhJQb8wFMl9PuZ/v27bz99tssWLCA4OBgXFxcMBgMpvLCwkJcXV0BcHFxoaioqMKyOxmKjVy4fPf9R+bya6qrkXrrAoldYrc2Ndtx4fKNWhW7msyJu8hQgou28hRjdvIZNGgQAIcPH76rTKPRcOLEifvW8eOPPzJjxgy++OILWrVqBUDLli1Nl9kAUlNTTYmstKxFixZ3lQkhhC2zlbEvs2/K8fLyomnTphX+5+Xldd/9CwsLeeutt5g7d64p8QBER0eTkJBAdnY2xcXFrFy5kieffNJUtnLlSkpKSrh8+TKJiYnExMSYG4oQQgiVmN3z2bVrl1n7JyUlkZOTw5tvvllu+/Llyxk6dCiDBg1CURS6du3KwIEDAYiLi+PcuXNERUVhb2/PiBEjaNu2rVntEEIIoR6zk09qauo9y+93OaxPnz706dOnwrK4uLgKbx51dHRkxowZVW+kEEKIWsXs5NOnT59Kp1dXdcxHCCFE/WJ28mnatGm55HPr1i1yc3Nxd3enefPm5lYvhBDCBtXImM+vv/7KuHHjeP75582tXghRz9jKbC5xbxZZTO5OzZo146mnnmLWrFlERkbWxEcIIYTqJDFajtnJ5+effy73WlEULl26xMKFC8nNzTW3eiGEEDbIIjeZVjThQFEUoqKizK1eiHpD/qoW9YnZySc0NPSu5OPm5kZoaCgvvPCCudULIapJkpmozcxOPqtWrbJEO4QQQtQj1Uo+mzZtqvJ7n3rqqep8hBA1QnoDQtQO1Uo+Y8aMqfK6PZJ8hBBC3KlayedeTzUQQggh7qdayWf27NmWbocQQoh6xCI3maalpbF69WrS0tKwt7enbdu2xMXF0aRJkyrXcevWLT744AO++OILdu7ciY+PDwCLFy9m9erVGI1GHnnkEaZMmYKTkxMGg4Fp06Zx4MAB7O3tiY2NrfAhpELUNtYedyr7+dZqg7g3a/+OqMHs5HPw4EFefPFFDAYDiqIAtx+5s2rVKr7++usqP99t+PDhPPTQQ+W2HTp0iKVLl7J+/Xp0Oh3Dhw9n+fLlvPjiiyxevJjr16+TkJDA9evX6devH6GhobRv397ckISoM9Q6SUnCEpZm9mJyc+bMQVEUXn31VeLj45k7dy4vvfQSer3+gZY9eO2113j99dfLbdu6dSsxMTE0bNgQOzs7Bg4cSEJCgqmsf//+2NnZ4eHhQVRUFFu3bjU3HCGEECowu+dz7NgxXnzxRf75z3+atj3xxBMoivJA9wD94Q9/uGtbRkYGPXv2NL0OCAgwLa2dnp5OYGCgqSwwMJCdO3dWJwQhhI2SnmHtZXbyURQFR0fHu7a7ublhNBrNqruwsBAnJyfTa2dnZwoLCwHQ6/VotdoKy8pycrDDr6nOrHZUxMnBvkbqrQtsJfbqxGDp2Curqzqfca99qltmyX2qQ63v5377VXTcLf2dVqeumj4O5vy+a53s71ludvJp3749X3zxBY0aNaJDhw4oisLhw4dZuHDhXWM4D8rFxQWDwWB6XVhYiKurq6msqKiowrKyDMVGLly+YVY7KuLXVFcj9dYFthJ7dWKwdOyV1VWdz7jXPtUtq8o+Ndm7qOr3U5U2VPe4Xbh8o8LjXtXv1Nzvx9LH7kGY8/teZCjBRVt5ijE7+UyYMIFnn332rvEde3t7xo8fb1bdLVu2NF1mg9tLdpcuy11a1qJFi7vKhBBC1G7VTj5JSUl0796d3/3ud2zcuJEFCxZw+vRpANq2bcvLL79sdjKIjo5mxIgRvPzyyzRq1IiVK1fy5JNPmspWrlxJ9+7dyc7OJjExkc8//9yszxO2oT5MU60p1hgjkWNU82rj913t5PPaa6/h4eFBTEwMTz/9NDNnzqx2I65evVpu1dPBgwdjb2/PkiVLGDp0KIMGDUJRFLp27crAgQMBiIuL49y5c0RFRWFvb8+IESNo27ZttdsgRG1WG08eQpij2snn97//PSdOnGDFihWsXLmS5s2b8/TTT/PnP/8ZPz+/B6qrSZMmlU6TjouLq/DmUUdHxweayi2EELasrv2BUu37fNatW8d3333HhAkTeOSRR8jKyuKjjz4iIiKC5557jq+++or8/HxLtlUIIYSNMGvCQbNmzUw9k2vXrpGUlMSOHTvYu3cvP//8M++88w6HDx+2VFuFEELYCIs82w3A3d2dPn364O7uTsOGDdm8eXO5adKiYnWtqyyEEJZgdvLJy8vju+++Y/v27ezevRu9Xo+iKLRv356+fftaoo1CCGGT6vMfn9VOPitXrmT79u3s37+fkpISFEXB19eXp556ir59+xIUFGTJdgpR68gjVe6vNp9ca3Pb6oNqJ5/p06cDtx+jExkZydNPP02XLl0s1jAhhBC2q9rJJzw8nKeffprIyEicnZ0t2SZRD9naX6G2Fo8Qllbt5LNw4UJLtkOICslTiYWoOdb8vTd7PR8hhBDiQVlsqrUQtqrsX4enpve2YkuEsB3S8xFCCKE66fkI1cggvBCilCQflciJ1/LkOxWi7pLk84BkVpQQQphPoyiKYu1G1BR/f3/y8vL5Xbv2Fqvz8IXr5V539Gv0wPuV3aey7fcre1CWrEvrZM/+9JwK67vX91OdWK2xz73KwoIaU2QoeaB96mqstnZczfl+tE72FBlKbPr7qWi/0rir4+TxozRs2IBffvmlwnKbTj6hoaFcuXJFltcWQgiVpaam4uXlRUpKSoXlNp18hBBC1E4y1VoIIYTqJPkIIYRQnSQfIYQQqpPkI4Qwi9FotHYTRB0kyUeYLTMzk4sXL1q7GVaxfft2Dh8+zLVr16zdFNVt2rSJy5cvY2dnR32bt3TgwAHOnj1Lfn6+tZtSZ9lPnTp1qrUbYQv27dvHjRs3KCkpQafTAaAoChqNxsotq1ljx47l22+/JT4+Ho1GQ0hICA4ODjYfN8Dw4cP54YcfOH/+PF26dMHNzc3aTVLN9u3bGTNmDEVFRbRp04YGDRrUi993gNGjR7Njxw6+/PJLcnJyaN68OY0amXfvXF1hyfOcPOHAAkaNGkVOTg55eXkEBQXRqVMnBg0ahEajsel/kO+++y43btzg008/Zfv27cyaNYvQ0FBCQ0Ot3bQal5SURH5+PmvWrAEgOzubzMxMnJ2dadq0qZVbV/PCwsJ46KGHAJg3bx5///vf8fX1paSkBHt7eyu3ruZ8+umnFBQUsGzZMvbv38/777/P73//ewICAqzdtBpn6fOcJB8zffXVV9y8eZPFixeTnp7OkSNHWLRoEdeuXWP48OE2m3gA9Ho9gwcPBiAiIoL9+/eTmpqKk5MTzZo1o3HjxlZuYc3R6/XY2d2+ar1w4UISEhLQ6/UEBwfTo0cP+vbta+UW1pzSMZ6SkhL8/f25evUqCxYs4F//+hclJSW4uLhYuYU158aNG6Y/rsLCwujVqxc7d+7kqaeeori4GAcH2zyl1sR5zja/KRUZDAZT9zMoKAgfHx8aNmzIBx98gJubG3/729+s3MKaYTAYuHTpEhs3biQkJIT8/HxWr15Nz549+eyzz+jZsyfPP/88/v7+1m5qjejQoQNbt25ly5Yt/Pjjj3z++edcuHCBQ4cOsW3bNlq2bEmHDh2s3cwaYWdnR8OGDYmMjCQoKIiuXbuSkJDAP/7xDy5dusSiRYto2LChTfWAiouLsbe3N/VsS3t47u7upoRjq4kHauY8JxMOzBQSEkJRURGnTp0CwMXFha5du/Lqq6+SnJxMWlqalVtoWTNnzmT//v04OTkxefJkjhw5wpQpU/jrX//KK6+8wocffsi7777Lnj17OHv2rLWba1Fr1qxh8+bN/PDDDwQEBODm5sbatWsJDAzE3d2ddu3a0b17dwoLCzl//ry1m2tRq1evZsuWLfzwww/ltm/YsIE2bdrg6enJvn378PLywtXV1aYSD2Aaxxw6dCjh4eGm+BRFwWAwmN73448/cvjwYWs1s8bUxHlOkk81rFixgm+++YakpCQeeeQRHB0dWbx4MZcuXQJAq9XSsWNHcnJyyMrKsnJrLSczM5N169YxceJE9u3bh5+fH6tWrWLGjBlERETw3HPPAdCpUyfat2/P1atXrdxiyxk9ejSbN2/m22+/5bPPPmPXrl2MHz8eg8HAgQMHOHLkCEajEX9/f4KCgsjOzrZ2ky1m9OjRbNmyhaSkJBYvXszXX38NQHR0NP7+/hw7doxPP/2U4cOH07p1az744ANu3bpl5VZbxr/+9S+GDRtmeu3s7IyXl5fptZOTk6lH8OWXX/Kf//wHT09P1dtZE2r6PCez3R7Q6NGjOX78OEVFRaxbt46TJ08yZswYVq5cyenTpwkKCsLd3R13d3eOHDmCq6uraWC2rmvUqBFZWVl4e3uzbNkygoKCaNOmDUajkY8++ogrV67wpz/9iXXr1rFx40aGDx9uE7OAtmzZwpEjR/jiiy8IDw/n/Pnz5OTk0L17dyIiIkhJSSElJYU9e/Zw/vx51q1bx6hRo3B3d7d2082WmJhISkoKX3zxBV26dOHSpUtkZWXRvXt3NBoNU6ZMYcmSJfznP//h2WefpUGDBvTo0YMGDRpYu+lmu3XrFl9++SXXrl3jhx9+IDo6Grg91lU63nfkyBGuXLmCwWDgiy++YNasWbRs2dKazbYINc5ztnuRsgYcPnyY69evs2jRIgCuXbvGgAED+Pe//81HH33EtGnTmDdvHu7u7gQFBfHDDz/w0ksvWbnVlmE0Gk33czz88MN069aNqVOnMn/+fEJCQpg3bx4DBgzg3LlzXL58mfj4eAIDA63dbIsoKioyXc9v2LAhbdq0Yd++fSxevBhHR0c+/vhjkpKS2L9/P1euXOGzzz6jefPmVm61ZRQWFlJUVATc/uMjLCyMKVOmcP36dRo1asS//vUvGjZsSOfOnQFsapzr0qVL/Prrr0ycOJFFixYxYsQI4uPjsbe3x2Aw4OTkRMeOHVmwYAEpKSnMnj2bkJAQazfbbKqd5xRRZcnJyUrPnj2V/Px80zaj0ahERkYq48ePVxRFURISEpS3335bmTx5snLmzBlrNbXG/Pjjj8o777yjKIqibN68WQkPD1dCQkKUwsJCJTs7W8nMzFSys7Ot3ErLKCkpURRFUX7++Wdl3bp1isFgULKzs5WePXsqb7/9tvLpp58qERERysyZM+/ap64rjePo0aPK2rVrlcLCQkVRFOXcuXPK888/f899bElycrKiKIpy8uRJ5YUXXlBee+21cuUFBQXKyJEjberfulrnOUk+VVD6j8poNCpvvPGGsmDBAtM/RkVRlPz8fCUmJkb5+uuvTdsMBoPq7bQ0o9F417ZDhw4pTz/9tKIoinLw4EElLCxM+dOf/qQcPXpU7ebVqDtjv3nzpqIoipKbm6vs2LHDtD0xMVEZO3asUlxcrGr7atKdsRcUFJh+Pn78uPLss8+aXq9fv15ZtWqVam2raaWxl/6bL3tc70xA33zzjbJ582b1G1lD1D7PyYSDKii93KTRaOjWrRunT59m48aN6PV6ANzc3IiOjubXX3817WML0y6V23+cMH/+fA4ePAhAx44d6dq1K6tXr+b1119nxowZvPnmm0ycOJGbN2/azGNWSu9biI+PJyUlBVdXVxRFwd3dnccff9wU5/nz58nLy6OkpHqrPdZGpcf9k08+ISUlpdx9O/b29qZY16xZw+eff06nTp2s1VSLKz3u8+bN4+effy43a69169aMGzcOo9FI7969mT9/vk0tVKn2eU4mHFTR+++/j0ajoUePHqSnp3P06FEuX75M69at0Wq1HDhwgF9//ZUePXoA1OmbSz/66COaNWuGh4cH165dw97e3nRNX1EUVq1axRdffMHMmTOJiIggJCSEmJgYGjRoUKfjBjh58iR6vR6tVgvcjvfRRx8Fbh9Tg8FASkoKmzdv5vjx46xevZq3337bJp5qcOdxd3BwICwsrNx7rl69SnJyMsXFxaxYsYLZs2fTpk0bK7XYcio67nfGbmdnR5MmTUhLS+Po0aPMmzfPJmIvS83znKxkWont27fTpEkTXFxcCAkJ4dSpU7Rt2xa4PQtm9erVHD58mKNHjxIZGcnmzZv57LPPaNWqlZVbbp4zZ87Qr18/QkJCmD17Ni1atDD9NVR6Y51er+f48eN06tTJ1AOo60kH4B//+Ae5ublkZ2cTHh5OeHi46R9Z6d3riqKwYcMGkpOTARg6dKhN/PV7r+Ou3PHolGeffZbs7GwWLFhgEyffqhz3UsnJyUycONFmEo81z3OSfCowevRoLl++jFarxWAw0KNHD1588UXg9swnrVaL0Wjk+vXrbN26Fa1WS2hoKEFBQVZuuWX885//pLi4mNTUVD755BNatWrFrVu3cHR0NM3yKXXniamuWrlyJbt27WLBggUcPXqUY8eOsWXLFp577jmioqKA/834K1X6ndiKex33srF/+eWXPPbYYzYxo68qx73s73haWhrOzs74+flZs9kWYe3zXN0fmLCwhIQE8vPzWbZsGdnZ2Zw6dYqxY8dy9epVxo4di1arNf015OHhwcCBA63dZIsqja1Pnz788ssvvPbaa6xduxYAR0dHnJyc+Omnn7h69SrR0dE2kXjg9uNDPDw8AGjfvj0NGzZkzZo1LF26FDs7OyIjI7Gzs2Pfvn3k5OQQExNjE+N68L/7Vu513Etj1+v1xMbGWrnFllNQUECTJk2Ayo+7RqMhOTmZ69evExkZaeUWW0ZtOM/JhIM7GAwGCgsLAfD09CQ8PJz58+ezdu1a4uPjgduDbD/++CNLly61ZlMtJjk5mRMnTpCbm4uDgwPdunXj8OHDvPDCC/z1r3/lySef5NFHH+Xy5cvcuHGDjIwM/vCHP1i72Raxd+9eTp48iaOjI25ubqSkpADQvHlzOnXqRM+ePTl48CA3b95Er9fzyy+/mB4saSuJ197eHo1GQ/fu3e953LOysmziUlNZvr6+5OXlceTIEaDy456VlUX79u2t3FrLqQ3nOZlw8P+VrlPh5ubGlStXKC4uNt2p7O3tzUMPPcTq1atp0aIFfn5+ZGRk0KlTJxo2bGjllptn4sSJbNmyhZMnTzJnzhzat2+Pvb0933zzDX/5y18oKSlhw4YNeHh4MHjwYBo0aEDbtm3rfNxwO/aEhATS0tJYtmwZhYWF5Obmsm3bNnbt2kVWVhaxsbEsWbKExx57DG9vb0JCQmwi9n/961/k5eXRtm1b02Wly5cvs2nTJvr164fRaGT9+vV3HXdbeGJF2dg9PT3ZtWsXhw4dYvfu3Xz//ff88ssvNnvca9N5TpIP/zsJnTx5kvnz59OgQQOKi4txdHTE19cXAJ1Ox4EDB2jatCkhISEEBgbW+V/Gr776ikOHDrFs2TKio6O5efMm8+fPJyoqiuLiYi5cuMDUqVOZOnUqvr6+fPTRR/Tt29cmFosrG3tkZCRubm4kJSXx0EMP0aBBAzw8PJg8eTLe3t4cOHCAkJAQmjVrVm7Mp67KzMwkPj6eTZs24e/vbxpgbt68OadOnSInJ4fJkyfb5HEvG7uvry+hoaE8+uijlJSUcPPmTTw9PW32uNe285xtXLQ2w1dffcWlS5f48ssvAVi8eDGffPIJ2dnZaDQabt68Sffu3fHw8MDLy6vcHPe6rrCw0HTiMRqNjBgxAhcXF8aOHcvNmzcBmD59Or179wZgwIAB5SYb1GV3xh4XF4e9vT1Lly5l5syZdOzYkZ07d3Lx4kX279/P6NGjrdxiy8nNzSUiIoJWrVoxduxYjEYjffv2Ra/Xc+LECZYtW0Z8fDwRERGAbR33srGPHz8eo9HIX/7yF/7617+a3rN7926ysrJs6rjXxvNcve/5HDx4EBcXF8LDwzEajYSGhuLh4UFCQgJGoxG9Xs/KlSu5cuUKGzZsYPTo0bi7u9f5vwDh9uSCTz/9lDZt2phWYnz44YdxcnIiMTGR6dOn8+STT5ouy9jSImEVxd6hQwfy8vKYMWMGfn5+7Nixg5SUFGbNmkWLFi2s22ALunXrFgUFBfTv3x9/f3/eeust/Pz8aN++PZ07dyYyMpI//vGPGI1GmzvuFcVetvd36NAh1q5dy88//2yacm4LauN5rt4nn4pOQu3atcPZ2Zk1a9YQFxcH3P5LefTo0XX+Pp6ymjZtSn5+Pnv27MHHxwcfHx/g9qyftLQ0WrduTevWrW1mOnVZlcUeFhbGsWPH8Pf3Z8SIETzxxBOmSxK2olGjRrRp0wZ7e3vatm1LQEAA48ePx8fHhy5dunD8+HEMBoNN3Dh7p4piL5uADh48yNNPP03//v1NvxO2oDae5+p98qnsJNSxY0dOnz5Ny5YtGTx4MGFhYaYpmbbCzs6OoKAgjh8/zk8//UTjxo1NJ9qdO3fSpEkT2rVrZ3OJB+4d+/fff4+3tzft2rXD0dHRJuO3t7c3PUbnd7/7HQEBAUyZMoWsrCxWrlxJv379TFPPbU1FsU+aNInMzExWrVrFn//853Jr9tiC2nieq/fJp6onYFsYcKyIm5sbbdu2JT09nRUrVpjm/G/fvp1XXnnFJtakqUxlsSclJZlit8XEU6o0ttKTcFZWFjt27GDJkiU21cOvSEWxf/fddyxdutQmY6+N5zl5wsH/99tvv7FixQp2795NREQEDRo0YNWqVcybN89mrvveS2FhIcnJyWzYsAF3d3diY2NtYm2SqqjPsZfauHEjH330EZ9++qlNPC7oQdSn2GvTeU6STxlyErKdx+VUR32OPSUlhQYNGtj8ybci9S322nKek+RTgfp8EhJC1A/WPs9J8hFCCKE62xxFF0IIUatJ8hFCCKE6ST5CCCFUJ8lHCCGE6iT5CCGEUJ0kHyGEEKqT5COEEEJ1/w+Ykk4oqPxM2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(df,volume=True)\n",
    "mpf.plot(df,volume=True,show_nontrading=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
